The detonation of Improvised Explosive Devices (IEDs) is a new and ongoing threat to both occupation ground forces and innocent civilians in war zones. IEDs can be constructed at a remote location and then transported and installed within a short period of time by a minimum number of opposition forces. To escape detection, IEDs are typically embedded into and appear as part of their local surrounding. Once installed, IEDs can be detonated autonomously or manually by an operator hidden nearby.
The current methods used to detect IEDs prior to their detonation requires one or more human image analysts to manually conduct a detailed and thorough review of an extensive database of imagery collected by one or more Unmanned Aerial Vehicles (UAV) or by other imaging means. Given the small size and camouflaged appearance of IEDs, the required image analyses may be tedious and can be overwhelming to a given set of image analysts. Therefore, there exists an unmet need for quickly and accurately determining the insertion of an IED into an area of interest through an analysis of multiple images containing a common area of interest.